Open AccessBayesian vector autoregressive model for multi‐subject effective connectivity inference using multi‐modal neuroimaging data
Author(s) -
Chiang Sharon,
Guindani Michele,
Yeh Hsiang J.,
Haneef Zulfi,
Stern John M.,
Vannucci Marina
Publication year - 2017
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23456
Subject(s) - autoregressive model , bayesian inference , inference , computer science , artificial intelligence , neuroimaging , bayesian probability , pattern recognition (psychology) , machine learning , resting state fmri , psychology , mathematics , statistics , neuroscience
Abstract In this article a multi‐subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting‐state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject‐ and group‐level. Furthermore, it accounts for multi‐modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject‐ and group‐level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting‐state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311–1332, 2017 . © 2016 Wiley Periodicals, Inc.